Agile estimation using people and project related factors

Popli, Rashmi; Chauhan, Naresh

doi:10.1109/indiacom.2014.6828023

Cited by 21 publications

(11 citation statements)

References 7 publications

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“…The factors related to the project include the type of project, quality requirements, hardware and software requirements, ease of operation, complexity, data transaction, and multiple sites. People-related factors consist of communication skills, familiarity with a team, managerial Skills, security, working time, experience with the previous project, and technical ability (Popli & Chauhan, 2014). Accuracy in the agile effort estimation relied heavily on the expertise of professionals in software development.…”

Section: Related Workmentioning

confidence: 99%

“…In the Agile effort estimation, the project factors are essential to determine. In addition, people factors are also important because the estimation process involves a team of developers from different disciplines (Munialo & Muketha, 2016;Popli & Chauhan, 2014). People-related factors represented each team's expertise, experience, domain knowledge, and technical ability (Popli & Chauhan, 2014).…”

Section: Development Of Path Coefficient Modelmentioning

confidence: 99%

“…In addition, people factors are also important because the estimation process involves a team of developers from different disciplines (Munialo & Muketha, 2016;Popli & Chauhan, 2014). People-related factors represented each team's expertise, experience, domain knowledge, and technical ability (Popli & Chauhan, 2014). For better results, estimation effort should include observance of various factors derived from software development methodology.…”

Section: Development Of Path Coefficient Modelmentioning

confidence: 99%

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Significant Factors in Agile Software Development of Effort Estimation

Sudarmaningtyas¹,

Mohamed²

2022

JST

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The Agile effort estimation involves project-related and people-related factors. This research objective is to find the factors that influence Agile effort estimation significantly through path analysis using a structural equation model. This research built an agile effort estimation path coefficient model from six constructs from theories and previous studies. Project-related factors represent by requirement and design implementation constructs. People-related factors are measured by the construct of experience, knowledge, and technical ability. The last construct is the effort itself. SmartPLS is employed for the confirmatory composite analysis and the structural model assessment. The confirmatory composite analysis indicated that all constructs are reliable and valid. Furthermore, the structural model assessment found that all factors of project-related constructs have a positive relationship and significant influence, showing a coefficient path value of 59.1% between requirement and design implementation constructs. All constructs represent people-related factors indicated by the coefficient path value of 67% between experience and knowledge, 42.6% between experience and technical ability, and 54.4% between knowledge and technical ability. In addition, all constructs proved influential simultaneously to effort by 31.1%. Positively contribute provided by requirement, experience, and technology’s ability. Significantly influenced provided by constructs of the developer’s knowledge and technical ability. The largest effect is given by technical ability, knowledge, and experience on medium and small scales. Contrarily, both constructs from project-related effects can be negligible because there was no influence. Based on the result, this study concludes that the significant factors in Agile effort estimation are technical ability, knowledge, and experience.

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Section: Related Workmentioning

confidence: 99%

Section: Development Of Path Coefficient Modelmentioning

confidence: 99%

Section: Development Of Path Coefficient Modelmentioning

confidence: 99%

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Significant Factors in Agile Software Development of Effort Estimation

Sudarmaningtyas¹,

Mohamed²

2022

JST

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“…Therefore, we compute the stability of a team (team-stability) and that of its product owner (po-stability). We also measure team-existence to quantify the familiarity and maturity of a team; both have been shown to lead to better team interactions and project performance in related work (e.g., [37], [38]). Previous studies (e.g., [39], [40]) have shown that the interactions among team members are less effective in distributed teams.…”

Section: B Risk Factor Extraction and Analysismentioning

confidence: 99%

Modeling Team Dynamics for the Characterization and Prediction of Delays in User Stories

Kula

Deursen

Gousios

2021

2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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In agile software development, proper team structures and effort estimates are crucial to ensure the on-time delivery of software projects. Delivery performance can vary due to the influence of changes in teams, resulting in team dynamics that remain largely unexplored. In this paper, we explore the effects of various aspects of teamwork on delays in software deliveries. We conducted a case study at ING and analyzed historical log data from 765,200 user stories and 571 teams to identify team factors characterizing delayed user stories. Based on these factors, we built models to predict the likelihood and duration of delays in user stories. The evaluation results show that the use of team-related features leads to a significant improvement in the predictions of delay, achieving on average 74%-82% precision, 78%-86% recall and 76%-84% F-measure. Moreover, our results show that team-related features can help improve the prediction of delay likelihood, while delay duration can be explained exclusively using them. Finally, training on recent user stories using a sliding window setting improves the predictive performance; our predictive models perform significantly better for teams that have been stable. Overall, our results indicate that planning in agile development settings can be significantly improved by incorporating team-related information and incremental learning methods into analysis/predictive models.

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“…Factors affecting Scrum-based project effort[35,36] What Are the Various Significant Factors Affecting Effort of Scrum Projects (RQ7)? Effort in Scrum projects has been widely affected by various people, resistance, and project factors.…”

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confidence: 99%

Estimation Approaches of Machine Learning in Scrum Projects: A Review

Dave¹

2021

IJRASET

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It is inevitable for any successful IT industry not to estimate the effort, cost, and duration of their projects. As evident by Standish group chaos manifesto that approx 43% of the projects are often delivered late and entered crises because of over budget and less required functions. Improper and inaccurate estimation of software projects leads to a failure, and therefore it must be considered in true letter and spirit. When Agile principle-based process models (e.g. Scrum) came into the market, a significant change can be seen. This change in culture proves to be a boon forstrengthening the collaboration betweendeveloper and customer.Estimation has always been challenging in Agile as requirements are volatile. This encourages researchersto work on effort estimation. There are many reasons for the gap between estimated and actual effort, viz., project, people, and resistance factors, wrong use of cost drivers, ignorance of regression testing effort, understandability of user story size and its associated complexity, etc. This paperreviewed the work of numerous authors and potential researchers working on bridging the gap of actual and estimated effort. Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation. Keywords: Machine Learning, Scrum, Scrum Projects, Effort Estimation, Agile Software Development

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Agile estimation using people and project related factors

Cited by 21 publications

References 7 publications

Significant Factors in Agile Software Development of Effort Estimation

Significant Factors in Agile Software Development of Effort Estimation

Modeling Team Dynamics for the Characterization and Prediction of Delays in User Stories

Estimation Approaches of Machine Learning in Scrum Projects: A Review

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